Mean Square Exponential Stability of Stochastic Cohen-Grossberg Neural Networks with Unbounded Distributed Delays
This paper addresses the issue of mean square exponential stability of stochastic Cohen-Grossberg neural networks (SCGNN), whose state variables are described by stochastic nonlinear integrodifferential equations. With the help of Lyapunov function, stochastic analysis technique, and inequality tech...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2010-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2010/513218 |
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| Summary: | This paper addresses the issue of mean square
exponential stability of stochastic Cohen-Grossberg neural
networks (SCGNN), whose state variables are described by
stochastic nonlinear integrodifferential equations. With the
help of Lyapunov function, stochastic analysis technique, and
inequality techniques, some novel sufficient conditions on mean
square exponential stability for SCGNN are given. Furthermore,
we also establish some sufficient conditions for checking
exponential stability for Cohen-Grossberg neural networks with
unbounded distributed delays. |
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| ISSN: | 1026-0226 1607-887X |